On the effects of class noise on spam detection accuracy
Muhammad Nadzir Marsono, A. Rafiq, Mohamed Watheq El-Kharashi, Farez Gebali · Penerbit UTM eBooks · 2007
Spam contributes to approximately two-thirds of the e-mail traffic over the Internet [9] and is fast becoming a major problem for IT users and network administrators. Spam costs billions in lost productivity [21] and results in more problems than mere annoyance of delayed and lost non-spam e-mails. Spam continuously evolves to circumvent spam control systems and is becoming much more sophisticated [13]. Naive Bayes classification has widely been used for spam detection and several variations have been proposed [1], [25], [11]. As other supervisedlearning techniques, its accuracy (for detecting spam) depends on the quality, quantity, and timeliness of the learning corpora